Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.769689
Title: Study of Higgs boson production through vector boson fusion at the CMS experiment using a dense convolutional neural network
Author: Wright, Jack Charles
ISNI:       0000 0004 7658 9798
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2019
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Abstract:
Measurements of the Higgs boson using the H → γγ Higgs boson decay mode and two different methods for identifying Higgs bosons produced via vector boson fusion are presented. These analyses use proton-proton collision data collected by the CMS collaboration during the 2016 running period and constitute 35.9fb⁻¹ of integrated luminosity at √s = 13 TeV. One vector boson fusion identification method is based on boosted decision trees, and the other is based on jets formulated as images and a dense convolutional neural network. The categorisations produced by both methods are subjected to the overall H → γγ statistical analysis and their results compared. The neural network itself is also subjected to analysis to determine what features it has learned to extract from the jet images. The main objectives of this new approach are to reduce contamination from gluon fusion in the vector boson fusion categories and to improve their statistical significance. This is indeed observed in the expected yields measured from simulation where vector boson fusion category signal purity and significance are both increased. The measurement of the vector boson fusion signal strength modifier is improved in the new approach where we observe reduced uncertainties. The value relative to the Standard Model is measured to be 0.8+0.6/-0.5 in the boosted decision tree variant and 1.5 +0.5/-0.5 in the neural network variant. The neural network is also observed to give a reduced uncertainty on many of the other measurements, especially those more directly impacted by vector boson fusion production.
Supervisor: Dauncey, Paul ; Seez, Chris Sponsor: Science and Technology Facilities Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.769689  DOI:
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